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Professor Valentina Corradi

Biography

Biography

Valentina Corradi obtained a PhD in Economics in 1994 at the University of California, San Diego. She held positions at University of Pennsylvania, Queen Mary-University of London, University of Exeter and University of Warwick.

Her work has been published on Journal of Econometrics, Econometric Theory, Journal of the American Statistical Association, Review of Economic Studies, International Economic Review and Journal of Monetary Economics.

In 2005, the Indian Government launched a conditional cash-incentive program to en- courage institutional delivery. This paper studies the e ects of the program on neonatal mortality using district-level household survey data. We model mortality using survival analysis, paying special attention to the substantial heaping present in the data. The main objective of this paper is to provide a set of sucient conditions for identi cation and consistent estimation of the baseline hazard accounting for heaping and unobserved heterogeneity. Our identi cation strategy requires neither administrative data nor mul- tiple measurements, but a correctly reported duration and the presence of some at segments in the baseline hazard which includes this correctly reported duration point. We establish the asymptotic properties of the maximum likelihood estimator and pro- vide a simple procedure to test whether the policy had (uniformly) reduced mortality. While our empirical ndings do not con rm the latter, they do indicate that accounting for heaping matters for the estimation of the baseline hazard.

Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. In order to address this issue, a novel criterion for forecast evaluation that utilizes the entire distribution of forecast errors is introduced. In particular, we introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority; and we develop tests for GL (CL) superiority that are based on an out-of-sample generalization of the tests introduced by Linton, Maasoumi, and Whang (2005, Review of Economic Studies 72, 735?765). Our test statistics are characterized by nonstandard limiting distributions, under the null, necessitating the use of resampling procedures to obtain critical values. Additionally, the tests are consistent and have nontrivial local power, under a sequence of local alternatives. The above theory is developed for the stationary case, as well as for the case of heterogeneity that is induced by distributional change over time. Monte Carlo simulations suggest that the tests perform reasonably well in finite samples, and an application in which we examine exchange rate data indicates that our tests can help identify superior forecasting models, regardless of loss function.

The specification of an optimizing model of the monetary transmission mechanism requires selecting a policy regime, commonly commitment or discretion. In this paper, we propose a new procedure for testing optimal monetary policy, relying on moment inequalities that nest commitment and discretion as two special cases. The approach is based on the derivation of bounds for inflation that are consistent with optimal policy under either policy regime. We derive testable implications that allow for specification tests and discrimination between the two alternative regimes. The proposed procedure is implemented to examine the conduct of monetary policy in the United States economy.

In this paper, we fill a gap in the financial econometrics literature, by developing a ?jump test? for the null hypothesis that the probability of a jump is zero. The test is based on realized third moments, and uses observations over an increasing time span. The test offers an alternative to standard finite time span tests, and is designed to detect jumps in the data generating process rather than detecting realized jumps over a fixed time span. More specifically, we make two contributions. First, we introduce our largely model free jump test for the null hypothesis of zero jump intensity. Second, under the maintained assumption of strictly positive jump intensity, we introduce a ?self excitement test? for the null of constant jump intensity against the alternative of path dependent intensity. The latter test has power against autocorrelation in the jump
component, and is a direct test for Hawkes diffusions (see e.g., Aït-Sahalia, Cacho-Diaz and Laeven (2015)). The limiting distributions of the proposed statistics are analyzed via use of a double asymptotic scheme, wherein the time span goes to infinity and the discrete interval approaches zero; and the distributions of the tests are normal and half normal, respectively. The results from a Monte Carlo study indicate that the tests have good finite sample properties.

In 2005, the Indian Government launched a conditional cash-incentive program to encourage institutional delivery. This paper studies the effects of the program on neonatal mortality using district-level household survey data. We model mortality using survival analysis, paying special attention to substantial heaping, a form of measurement error, present in the data. The main objective of this paper is to provide a set of sufficient conditions for identification and consistent estimation of the (discretized) baseline hazard accounting for heaping and unobserved heterogeneity. Our identification strategy requires neither administrative data nor multiple measurements, but a correctly reported duration point and the presence of some flat segment(s) in the baseline hazard. We establish the asymptotic properties of the maximum likelihood estimator and derive a set of specification tests that allow, among other things, to test for the presence of heaping and to compare different heaping mechanisms. Our empirical findings do not suggest a significant reduction of mortality in treated districts. However, they do indicate that accounting for heaping matters for the estimation of the hazard parameters.

This paper develops statistical tools for testing conditional independence among the jump components of
the daily quadratic variation, which we estimate using intraday data. To avoid sequential bias distortion, we
do not pretest for the presence of jumps. If the null is true, our test statistic based on daily integrated jumps
weakly converges to a Gaussian random variable if both assets have jumps. If instead at least one asset
has no jumps, then the statistic approaches zero in probability. We show how to compute asymptotically
valid bootstrap-based critical values that result in a consistent test with asymptotic size equal to or smaller
than the nominal size. Empirically, we study jump linkages between US futures and equity index markets.
We find not only strong evidence of jump cross-excitation between the SPDR exchange-traded fund and
E-mini futures on the S&P 500 index, but also that integrated jumps in the E-mini futures during the
overnight period carry relevant information.